变量聚类法

变量聚类法

clc,clear
a=[ 3.248099862 	1400.268343019 	1595.279262	5950.668859	653.767546	388.6101631	0.357295727	18320.46154	0.000181229 	83235.96118	11701.2957
    0.313842461 	159.444173537 	177.7462234	662.2847022	70.01836837	42.60477173	0.342129003	20970	0.000008924 	2628	771.0150925
    0.466173300 	257.271504590 	265.1189989	1025.518975	94.97690194	61.78586048	0.323605467	21921.75	0.000022727 	914	1642.154022
    0.653314088 	193.740207541 	257.3170485	955.9373388	79.74565749	56.26133025	0.33415978	14504.66667	0.000012397 	339	633.8301523
    4.777377094 	1890.965552478 	2082.616795	8007.801239	932.9081486	558.0550182	0.327117741	16111.6	0.000016287 	4840.373816	860.0313942
    0.274618332 	118.983750840 	126.9446139	465.0666947	48.56818687	31.38656774	0.416476625	16795.5	0.000012747 	3271	389.5885042
    0.227587522 	79.673939077 	103.4399852	402.6383542	35.18759255	21.34329384	0.314754098	17450	0.000002348 	887.733767	330.9389993
    0.416676302 	169.501844080 	194.3978197	776.6916763	59.72942049	44.16420549	0.320140105	18562.5	0.000011300 	8691.278086	591.0445205
    0.275472987 	115.688675169 	138.2225522	546.2145303	39.05687346	31.59174764	0.341489362	19719	0.000010870 	3565.464982	342.6701571
    0.224227313 	173.672287944 	185.1560534	692.5092589	85.71248786	47.12613714	0.314630309	30815	0.000010066 	2608.055401	1917.304038
    0.642387833 	366.064706442 	402.7450517	1551.719929	240.1888106	118.1672418	0.227734688	24155.5	0.000023164 	7151.532134	2759.019264
    0.216517796 	47.380797240 	39.30549861	129.1619382	11.69526008	9.253392589	0.620879121	6030	0.000000266 	648.8938883	19.44754811
    0.212064205 	72.389522219 	68.70408516	275.8842798	18.86692494	14.42764848	0.304106548	13175	0.000001608 	789.8	8.464149342
];
b=zscore(a)%标准化处理
r=corrcoef(b);%相关系数
d=tril(1-r);
d=nonzeros(d)';
z=linkage(d,'average')
h=dendrogram(z);
set(h,'Color','k','LineWidth',1.3)
T=cluster(z,'maxclust',6)
for i=1:6
    tm=find(T==i);
    tm=reshape(tm,1,length(tm));
    fprintf('第%d类的有%s\n',i,int2str(tm));
end

最短距离-类间分类

b=[7.9	39.77	8.49	12.94	19.27	11.05	2.04	13.29
7.68	50.37	11.35	13.3	19.25	14.59	2.75	14.87
9.42	27.93	8.2	8.14	16.17	9.42	1.55	9.76
9.16	27.98	9.01	9.32	15.99	9.1	1.82	11.35
10.06	28.64	10.52	10.05	16.18	8.39	1.96	10.81];

d1=pdist(b); %欧氏距离：b中每行之间距离
% 五种类间距离聚类
z1=linkage(d1);
z2=linkage(d1,'complete');
z3=linkage(d1,'average');
z4=linkage(d1,'centroid');
z5=linkage(d1,'ward');
H= dendrogram(z1) %作谱系聚类图
% 输出分类结果
T=cluster(z1,3)
% 结果表明：若分为三类，则辽宁是一类，浙江是一类，河南、青海和甘肃是另一类。

参考链接

数学建模之聚类分析